Non-Invasive Neural Technology in Analysing Spinal Cord Activity
A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neural Engineering, Neuroergonomics and Neurorobotics".
Deadline for manuscript submissions: 20 February 2026 | Viewed by 6
Special Issue Editor
2. Neuroscience and Behaviour Laboratory, Italian Institute of Technology (IIT), 00161 Rome, Italy
Interests: physiology; machine learning; artificial intelligence; human–computer interaction; neuroimaging
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Despite the advantage that intrathecal recording techniques are safe, recording the activity of the spinal cord at the cutaneous level offers several advantages. First, these methods can be more comfortably applied to human participants. Second, cutaneous-level recordings are relatively easy to perform. Third, recordings at the skin level are cost-effective, as they do not require specialized equipment or training and can rely on reusable, non-disposable electrodes.
However, the possibility of recording spinal cord activity non-invasively is hindered by several technical challenges, such as cardiac-induced pulsatile movement of the spinal cord, differences in the magnetic susceptibilities of cartilage, tissue, and bone, and motion artifacts caused by cerebrospinal fluid flow.
The aim of this Special Issue is to provide a comprehensive overview of the recent developments in technologies employed for the non-invasive recording and analysis of spinal cord activity. We welcome the submission of original research articles and review papers focusing on, but not limited to, non-invasive recordings of spinal cord activity, novel electrode designs, signal processing techniques, artifact reduction strategies, machine learning applications, wearable neurotechnologies, clinical applications in neurorehabilitation, and translational studies that bridge experimental and human research.
Dr. Giulio Gabrieli
Guest Editor
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Keywords
- spinal cord
- non-invasive recording
- electrophysiology
- neurotechnology
- signal processing
- artifact reduction
- wearable sensors
- machine learning
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